Mathematical Modeling of Optimal Drone Flight Trajectories for Enhanced Object Detection in Video Streams Using Kolmogorov–Arnold Networks by Aida Issembayeva, Oleksandr Kuznetsov, Anargul Shaushenova, Ardak Nurpeisova, Gabit Shuitenov, Maral Ongarbayeva

“…While most research focuses on improving detection algorithms, the relationship between flight parameters and detection performance remains poorly understood. …”
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Machine Learning Performance Analysis for Bagging System Improvement: Key Factors, Model Optimization, and Loss Reduction in the Fertilizer Industry by Ari Primantara, Udisubakti Ciptomulyono, Berlian Al Kindhi

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Designing Predictive Analytics Frameworks for Supply Chain Quality Management: A Machine Learning Approach to Defect Rate Optimization by Zainab Nadhim Jawad, Balázs Villányi

“…The framework employs advanced ML algorithms, including extreme gradient boosting (XGBoost), support vector machines (SVMs), and random forests (RFs), to accurately predict defect rates and derive actionable insights for supply chain optimization. …”
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Exploring the best fit: A comparative analysis of AFINN, Textblob, VADER, and Pattern on Arabic reviews for optimal dictionary extraction by Shakeel Ahmad, Sheikh Muhammad Saqib, Asif Hassan Syed, Nashwan Alromema, Ali Kararay

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Enhanced supervised classification of seasonal pastures on the Qinghai-Tibet Plateau (1990–2020) using Landsat optimal time window by Panfei Fang, Jia Wang, Haifeng Xu, Ruonan Li, Long Yu, Shaodong Huang, Yuying Liang, Pengfei Zheng

“…The framework leverages Landsat imagery within the optimal time window (Days of Year, DOYs 190–280), incorporates algorithms for automated sample generation and refinement, and employs the Random Forest (RF) classifier to enable fully automated seasonal pasture mapping. …”
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Optimization of guidelines for Risk Of Recurrence/Prosigna testing using a machine learning model: a Swedish multicenter study by Una Kjällquist, Nikos Tsiknakis, Balazs Acs, Sara Margolin, Luisa Edman Kessler, Scarlett Levy, Maria Ekholm, Christine Lundgren, Erik Olsson, Henrik Lindman, Antonios Valachis, Johan Hartman, Theodoros Foukakis, Alexios Matikas

“…Purpose: Gene expression profiles are used for decision making in the adjuvant setting in hormone receptor-positive, HER2-negative (HR+/HER2-) breast cancer. While algorithms to optimize testing exist for RS/Oncotype Dx, no such efforts have focused on ROR/Prosigna. …”
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Hybrid optimization for sustainable design and sizing of standalone microgrids integrating renewable energy, diesel generators, and battery storage with environmental consideration... by Mohd Bilal, Pitshou N. Bokoro, Gulshan Sharma

“…This study proposes the Hybrid Particle Whale Optimization Algorithm (HPWOA), combining Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA), to overcome the convergence issues of traditional methods. …”
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Characteristics prediction and optimization for fan duct surface heat exchanger using regional heat transfer correlation and NSWOA aided by Sobol’ by Zhe Xu, Zongling Yu, Xin Ning, Changyin Zhao, Zhihua Zhu, Zhibin Feng

“…A heat transfer unit response model between nine configuration parameters and heat transfer efficiency, weight is constructed by combining Taguchi and Particle Swarm Optimization-trained Adaptive Neuro-Fuzzy Inference System, and based on it, a characteristics optimization method based on Non-dominated Sorting Whale Optimization Algorithm aided by Sobol’ is proposed to simultaneously realize heat transfer enhancement, flow resistance reduction, and lightweight design. …”
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Transactive energy management for efficient scheduling and storage utilization in a grid-connected renewable energy-based microgrid by Peter Anuoluwapo Gbadega, Olufunke Abolaji Balogun

“…This study presents an advanced Transactive Energy Management (TEM) approach employing the Slime Mould Algorithm (SMA) to optimize scheduling and storage utilization in grid-connected renewable energy microgrids. …”
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Advancing multi-categorization and segmentation in brain tumors using novel efficient deep learning approaches by Nadenlla RajamohanReddy, G. Muneeswari

“…Using the Binary Chaotic Transient Search Optimization (BCTSO) Algorithm, the most pertinent features in terms of shape, texture, and colour are chosen to minimize complexity. …”
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A Multi-Objective Sensor Placement Method Considering Modal Identification Uncertainty and Damage Detection Sensitivity by Xue-Yang Pei, Yuan Hou, Hai-Bin Huang, Jun-Xing Zheng

“…To address this challenge, this study proposes a multi-objective sensor placement optimization method based on the Non-Dominated Sorting Genetic Algorithm. …”
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Real-Time Estimation of the State of Charge of Lithium Batteries Under a Wide Temperature Range by Da Li, Lu Liu, Chuanxu Yue, Xiaojin Gao, Yunhai Zhu

“…Additionally, the cat swarm optimization (CSO) algorithm optimizes the noise covariance matrices of both filters, thereby enhancing the precision of parameter identification and <i>SOC</i> estimation. …”
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A Judging Scheme for Large-Scale Innovative Class Competitions Based on Z-Score Pro Computational Model and BP Neural Network Model by Zhuoting Yu, Hongzhong Deng, Shuaiwen Tang

“…The model employs a genetic algorithm to maximize the overlap between experts while ensuring a balanced distribution of evaluation tasks, thus reducing the entropy generated by imbalances in the process. …”
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A Bi-Layer Collaborative Planning Framework for Multi-UAV Delivery Tasks in Multi-Depot Urban Logistics by Junfu Wen, Fei Wang, Yebo Su

“…The novelty of this work lies in the seamless integration of an enhanced genetic algorithm and tailored swarm optimization within a unified two-tier architecture. …”
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Toward model-based individualized fitting of hip-flexion exosuits for persons with unilateral transfemoral amputation by Finn G. Eagen, Nicholas P. Fey

“…A user-specific device profile is generated via a three-axis moment-matching optimization using an interior-point algorithm. We employ two related cost functions that reflect an active and passive form of the device. …”
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Improved grey wolf optimization for parameter extraction of polycrystalline and mono crystalline solar PV panels using double diode model by Madhusudana Rao Ranga, Venkateswara Rao Bathina, Srikumar Kotni

“…In this paper an enhanced version of the Grey Wolf Optimizer (GWO) algorithm called Improved Grey Wolf Optimizer (IGWO) has been proposed for extraction of parameters. …”
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Stiffness Optimization for Hybrid Electric Vehicle Powertrain Mounting System in the Context of NSGA II for Vibration Decoupling and Dynamic Reaction Minimization by Zhanpeng Fang, Qihang Li, Lei Yao, Xiaojuan Hu

“…The 6-DOF decoupling of the whole suspension system is optimized and the dynamic reaction transmitted to the body is minimized. …”
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An Optimization Framework for Waste Treatment Center Site Selection Considering Nighttime Light Remote Sensing Data and Waste Production Fluctuations by Junbao Xia, Yanping Liu, Haozhong Yang, Guodong Zhu

“…Using Beijing as a case study, the gradient boosting regression algorithm yielded a prediction accuracy of 92%. Furthermore, in light of the substantial costs associated with waste recovery route planning and site selection for treatment facilities, this research further devised a location and distribution framework for waste treatment centers based on high-precision predictions of waste production while employing multi-objective evolutionary algorithms (MOEAs) alongside the non-dominated sorting genetic algorithm II (NSGA-II) for optimization. …”
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Research on unmanned mining truck path planning based on grey wolf optimization adaptive hybrid A* and artificial potential field by HAN Yanling, LYU Jinke, ZHOU Guofeng, ZHANG Like, ZHANG Yun

“…The proposed method emploied the grey wolf optimization algorithm to adaptively adjust the key parameters of the hybrid A*(HA*) algorithm, achieving a multi-objective balance among path length, smoothness, and planning time. …”
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SABO-ELM model for remaining life prediction of lithium-ion batteries under multiple health factors by Jiabo LI, Zhonglin SUN, Di TIAN, Zhixuan WANG

“…The SABO algorithm optimizes the weights and bias thresholds of the ELM model, which effectively reduces the risk of local optima and improves its predictive performance and stability. …”
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